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Nowadays, the design of optimization algorithms is very popular to solve problems in various scientific fields. The optimization algorithms usually inspired by the natural behaviour of an agent, which can be humans, animals, plants, or a physical or chemical agent. Most of the algorithms proposed in the last decade inspired by animal behaviour. In this article, we present a new optimizer algorithm called the wild horse optimizer (WHO), which is inspired by the social life behaviour of wild horses. Horses usually live in groups that include a stallion and several mares and foals. Horses exhibit many behaviours, such as grazing, chasing, dominating, leading, and mating. A fascinating behaviour that distinguishes horses from other animals is the decency of horses. Horse decency behaviour is such that the foals of the horse leave the group before reaching puberty and join other groups. This departure is to prevent the father from mating with the daughter or siblings. The main inspiration for the proposed algorithm is the decency behaviour of the horse. The proposed algorithm was tested on several sets of test functions such as CEC2017 and CEC2019 and compared with popular and new optimization methods. The results showed that the proposed algorithm presented very competitive results compared to other algorithms. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/90787-wild-horse-optimizer.
Iraj Naruei; Farshid Keynia. Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Engineering with Computers 2021, 1 -32.
AMA StyleIraj Naruei, Farshid Keynia. Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Engineering with Computers. 2021; ():1-32.
Chicago/Turabian StyleIraj Naruei; Farshid Keynia. 2021. "Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems." Engineering with Computers , no. : 1-32.
Recently, many intelligent algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search volatile and multi-dimensional solution spaces and find optimal answers timely. In this paper, a new meta-heuristic method is proposed that inspires the behavior of the swarm of birds called Coot. The Coot algorithm imitates two different modes of movement of birds on the water surface: in the first phase, the movement of birds is irregular, and in the second phase, the movements are regular. The swarm moves towards a group of leading leaders to reach a food supply; the movement of the end of the swarm is in the form of a chain of coots, each of coot which moves behind its front coots. The algorithm then runs on a number of test functions, and the results are compared with well-known optimization algorithms. In addition, to solve several real problems, such as Tension/Compression spring, Pressure vessel design, Welded Beam Design, Multi-plate disc clutch brake, Step-cone pulley problem, Cantilever beam design, reducer design problem, and Rolling element bearing problem this algorithm is used to confirm the applicability of this algorithm. The results show that this algorithm is capable to outperform most of the other optimization methods. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/89102-coot-optimization-algorithm.
Iraj Naruei; Farshid Keynia. A new optimization method based on COOT bird natural life model. Expert Systems with Applications 2021, 183, 115352 .
AMA StyleIraj Naruei, Farshid Keynia. A new optimization method based on COOT bird natural life model. Expert Systems with Applications. 2021; 183 ():115352.
Chicago/Turabian StyleIraj Naruei; Farshid Keynia. 2021. "A new optimization method based on COOT bird natural life model." Expert Systems with Applications 183, no. : 115352.
In this research, a new method for population initialisation in meta‐heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta‐heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm from trapping in the local optima. Therefore, the starting point of a meta‐heuristic algorithm can have a significant impact on the performance and output results of the algorithm. In this research, using the Pareto 80/20 rule, an innovative and new method for creating an initial population in meta‐heuristic algorithms is presented. In this method, by using elitism, it is possible to increase the convergence of the algorithm toward the global optima, and by using the complete distribution of the population in the search spaces, the algorithm is prevented from trapping in the local optima. In this research, the proposed initialisation method was implemented in comparison with other initialisation methods using the cuckoo search algorithm. In addition, the efficiency and effectiveness of the proposed method in comparison with other well‐known initialisation methods using statistical tests and in solving a variety of benchmark functions including unimodal, multimodal, fixed dimensional multimodal, and composite functions as well as in solving well‐known engineering problems was confirmed.
Mohammad Reza Hasanzadeh; Farshid Keynia. A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms. IET Software 2021, 1 .
AMA StyleMohammad Reza Hasanzadeh, Farshid Keynia. A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms. IET Software. 2021; ():1.
Chicago/Turabian StyleMohammad Reza Hasanzadeh; Farshid Keynia. 2021. "A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms." IET Software , no. : 1.
Security and continuous proper functioning of the power system is a vital issue for supplying demands. Providing an acceptable level of reliability and necessity of that in all parts of the power system, from generation to distribution, has always been, and remains a major concern for network managers. Asset management and performing regular and routine maintenance activities has a considerable impact on ensuring system reliability, reducing expensive crashes, and preventing shutdowns. Up to now, much research has been done on the proper planning of maintenance in different parts of the network with a focus on cost savings, improved system performance and reliability, and reduced shutdowns. The purpose of this article is to review the findings of the study and to provide a more comprehensive view of the maintenance planning issue.
Mina Mirhosseini; Farshid Keynia. Asset management and maintenance programming for power distribution systems: A review. IET Generation, Transmission & Distribution 2021, 15, 2287 -2297.
AMA StyleMina Mirhosseini, Farshid Keynia. Asset management and maintenance programming for power distribution systems: A review. IET Generation, Transmission & Distribution. 2021; 15 (16):2287-2297.
Chicago/Turabian StyleMina Mirhosseini; Farshid Keynia. 2021. "Asset management and maintenance programming for power distribution systems: A review." IET Generation, Transmission & Distribution 15, no. 16: 2287-2297.
Nowadays, a basic commodity for a human being to lead a standard lifestyle with human comfort irrespective of the nature of environmental conditions is electric power. The electricity load demand increases tremendously especially for a metropolitan city due to climatic conditions, population growth, local area development, industries expansion, air pollution, thermal device usage, etc. Hence, the accuracy of electricity load and its price forecasting is a deciding factor for the power distribution network to retain as an efficient, sustainable, and secure consumer-friendly network. On the other hand, based on the volatile, intermittent, and uncertain behavior of electricity load and price, an accurate, and robust forecast model should be designed. In this paper, a new hybrid forecast model for short-term electricity load and price prediction has been developed. The proposed method includes three modules: wavelet transform that is used to eliminate fluctuation behaviors of the electricity load and price time series, feature selection based on entropy and mutual information has been proposed to rank candidate inputs and eliminate redundant inputs according to their information value, and a new learning algorithm. The proposed learning method consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions. The performance of the proposed method has been validated successfully on load and price data collected from the Pennsylvania-New Jersey-Maryland (PJM) and Spain electricity markets. Also, for further test, the load data in Iran have been used.
Gholamreza Memarzadeh; Farshid Keynia. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electric Power Systems Research 2020, 192, 106995 .
AMA StyleGholamreza Memarzadeh, Farshid Keynia. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electric Power Systems Research. 2020; 192 ():106995.
Chicago/Turabian StyleGholamreza Memarzadeh; Farshid Keynia. 2020. "Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm." Electric Power Systems Research 192, no. : 106995.
Electricity price forecasting is a key aspect for market participants to maximize their economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non-stationarity, the trend of the price is usually complicated to predict. On the other hand, the accuracy of short-term electricity price and load forecasting is fundamental for an efficient management of electric systems. An accurate prediction can benefit future plans and economic operations of the power systems’ operators. In this paper, a new and accurate combined model has been proposed for short-term load forecasting and short-term price forecasting in deregulated power markets. It includes variational mode decomposition, mix data modeling, feature selection, generalized regression neural network and gravitational search algorithm. A mixed data model for the price and load forecast has been considered and integrated with the original signal series of price and load and their decomposition. Throughout this model, the candidate input variables are chosen by a distinct hybrid feature selection. Two reliable electricity markets (Pennsylvania-New Jersey-Maryland and Spanish electricity markets) have been used to test the proposed forecasting model and the obtained results have been compared with different valid benchmark prediction models. Lastly, the real load data of Favignana Island's power grid have been considered to test the proposed model. The obtained results pinpointed that the proposed model’s precision and stability is higher than in other benchmark forecasting models.
Azim Heydari; Meysam Majidi Nezhad; Elmira Pirshayan; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Applied Energy 2020, 277, 115503 .
AMA StyleAzim Heydari, Meysam Majidi Nezhad, Elmira Pirshayan, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Applied Energy. 2020; 277 ():115503.
Chicago/Turabian StyleAzim Heydari; Meysam Majidi Nezhad; Elmira Pirshayan; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. 2020. "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm." Applied Energy 277, no. : 115503.
The expansion of distributed generations (DG) in the distribution networks brings visible technical and economic benefits to the grid. On the other hand, the place and size for DGs in a grid are very important, and optimal selection can decrease voltage profile volatility, system losses, and increase reliability indices especially. The issue of reliability in the distribution system is one of the important parameters in selecting the optimal location of DG. In other words, by considering this parameter, DG location will change in the distribution system and by installing it at this point; the reliability of the network has improved. Therefore, this paper proposes an analytical index to determine the optimal size and siting of DGs in a distribution network. The proposed index has consisted of loss sensitivity factor, voltage stability index, and reliability based factors. To evaluate the efficiency of the proposed method in the previous section, the simulation procedure has been implemented on the IEEE 33 and 69 bus distribution systems. The effectiveness of the suggested index is clarified by using different test systems, and the results have been compared with other proposed methods. The results indicated that the proposed simple index could be acceptable in finding the optimal location and size of DG better than complicated optimization methods.
Gholamreza Memarzadeh; Farshid Keynia. A newindex‐basedmethod for optimalDGplacement in distribution networks. Engineering Reports 2020, 2, 1 .
AMA StyleGholamreza Memarzadeh, Farshid Keynia. A newindex‐basedmethod for optimalDGplacement in distribution networks. Engineering Reports. 2020; 2 (10):1.
Chicago/Turabian StyleGholamreza Memarzadeh; Farshid Keynia. 2020. "A newindex‐basedmethod for optimalDGplacement in distribution networks." Engineering Reports 2, no. 10: 1.
With the increasing number of population and the rising demand for electricity, providing safe and secure energy to consumers is getting more and more important. Adding dispersed products to the distribution network is one of the key factors in achieving this goal. However, factors such as the amount of investment and the return on the investment on one side, and the power grid conditions, such as loss rates, voltage profiles, reliability, and maintenance costs, on the other hand, make it more vital to provide optimal annual planning methods concerning network development. Accordingly, in this paper, a multilevel method is presented for optimal network power expansion planning based on the binary dragonfly optimization algorithm, taking into account the distributed generation. The proposed objective function involves the minimization of the cost of investment, operation, repair, and the cost of reliability for the development of the network. The effectiveness of the proposed model to solve the multiyear network expansion planning problem is illustrated by applying them on the 33-bus distribution network and comparing the acquired results with the results of other solution methods such as GA, PSO, and TS.
Mohammad Kakueinejad; Azim Heydari; Mostafa Askari; Farshid Keynia. Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Applied Sciences 2020, 10, 4795 .
AMA StyleMohammad Kakueinejad, Azim Heydari, Mostafa Askari, Farshid Keynia. Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Applied Sciences. 2020; 10 (14):4795.
Chicago/Turabian StyleMohammad Kakueinejad; Azim Heydari; Mostafa Askari; Farshid Keynia. 2020. "Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm." Applied Sciences 10, no. 14: 4795.
In recent years, clean energies, such as wind power have been developed rapidly. Especially, wind power generation becomes a significant source of energy in some power grids. On the other hand, based on the uncertain and non-convex behavior of wind speed, wind power generation forecasting and scheduling may be very difficult. In this paper, to improve the accuracy of forecasting the short-term wind speed, a hybrid wind speed forecasting model has been proposed based on four modules: crow search algorithm (CSA), wavelet transform (WT), Feature selection (FS) based on entropy and mutual information (MI), and deep learning time series prediction based on Long Short Term Memory neural networks (LSTM). The proposed wind speed forecasting strategy is applied to real-life data from Sotavento that is located in the south-west of Europe, in Galicia, Spain, and Kerman that is located in the Middle East, in the southeast of Iran. The presented numerical results demonstrate the efficiency of the proposed method, compared to some other existing wind speed forecasting methods.
Gholamreza Memarzadeh; Farshid Keynia. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Conversion and Management 2020, 213, 112824 .
AMA StyleGholamreza Memarzadeh, Farshid Keynia. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Conversion and Management. 2020; 213 ():112824.
Chicago/Turabian StyleGholamreza Memarzadeh; Farshid Keynia. 2020. "A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets." Energy Conversion and Management 213, no. : 112824.
Cognitive radio network (CRN) emerged to utilize the frequency bands efficiently. To use the frequency bands efficiently without any interference on the licensed user, detection of the frequency holes is the first step, which is called spectrum sensing in the context. In order to increase the quality of local spectrum sensing results, cooperative spectrum sensing (CSS) is introduced in the literature to combine the local sensing results. Recently, machine learning techniques are designed to improve the classification of the images and signals. Specifically, Deep Reinforcement Learning (DRL) is of interest for its substantial improvement in the classification problems. In this paper, we have proposed DRL based CSS algorithm, which is employed to decrease the signaling in the network of SUs. The simulation results represent the superiority of the proposed approach to state-of-the-art approaches, including Deep Cooperative Sensing (DCS), K-out-of-N, and Support Vector Machine (SVM) based CSS algorithms.
Rahil Sarikhani; Farshid Keynia. Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach. IEEE Communications Letters 2020, 24, 1459 -1462.
AMA StyleRahil Sarikhani, Farshid Keynia. Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach. IEEE Communications Letters. 2020; 24 (7):1459-1462.
Chicago/Turabian StyleRahil Sarikhani; Farshid Keynia. 2020. "Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach." IEEE Communications Letters 24, no. 7: 1459-1462.
Electricity load forecasting has been developed as an important issue in the deregulated power system in recent years. Many researchers have been working on the prediction of daily peak load for next month as an important type of mid-term load forecasting (MTLF). Nowadays, MTLF provides useful information for assessing environmental impacts, maintenance scheduling, adequacy assessment, scheduling of fuel supplies and limited energy resources etc. The characteristics of mid-term load signal, such as its non-stationary, volatile and non-linear behaviour, present serious challenges for this forecasting. On the other hand, many input variables and relative parameters can affect the load pattern. In this study, a new composite method based on a multi-layer perceptron neural network and optimisation techniques has been proposed to solve the MTLF problem. The proposed method has an optimal training algorithm composed of two search algorithms (particle swarm optimisation and improved ant lion optimiser) and a multi-layer perceptron neural network. The accuracy of the proposed forecast method is extensively evaluated based on several benchmark datasets.
Mostafa Askari; Farshid Keynia. Mid‐term electricity load forecasting by a new composite method based on optimal learning MLP algorithm. IET Generation, Transmission & Distribution 2020, 14, 845 -852.
AMA StyleMostafa Askari, Farshid Keynia. Mid‐term electricity load forecasting by a new composite method based on optimal learning MLP algorithm. IET Generation, Transmission & Distribution. 2020; 14 (5):845-852.
Chicago/Turabian StyleMostafa Askari; Farshid Keynia. 2020. "Mid‐term electricity load forecasting by a new composite method based on optimal learning MLP algorithm." IET Generation, Transmission & Distribution 14, no. 5: 845-852.
With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power plant (VPP) resolves an important part of these issues. This paper proposes a risk‐based two‐stage stochastic optimization framework to address the energy management problem for a VPP. The objective of the proposed framework is to optimize the operation of a VPP in day‐ahead (DA) and real‐time (RT) markets. In order to include the risk parameter in the proposed decision‐making problem, conditional value at risk (CVaR) index is applied in the objective function. The considered uncertain parameters in the model are price in DA market, as well as wind and solar generation for the next day. Markov chain Monte Carlo (MCMC) method is applied to model these uncertain parameters through generation of different scenarios. Also, the effects of using ESS on daily operation of considered VPP is investigated. The performance of the proposed method is illustrated through a case study using real data. The obtained results guarantee the appropriate operation of a VPP considering different values for level of the risk.
Mohammadreza Emarati; Farshid Keynia; Masoud Rashidinejad. A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant. International Transactions on Electrical Energy Systems 2019, 30, 1 .
AMA StyleMohammadreza Emarati, Farshid Keynia, Masoud Rashidinejad. A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant. International Transactions on Electrical Energy Systems. 2019; 30 (3):1.
Chicago/Turabian StyleMohammadreza Emarati; Farshid Keynia; Masoud Rashidinejad. 2019. "A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant." International Transactions on Electrical Energy Systems 30, no. 3: 1.
This paper proposes a novel hybrid strategy based on intelligent approaches to forecast electricity consumptions. The proposed forecasting strategy includes three main steps: (a) the evaluation of a correlation coefficient for socio-economic indicators on electric energy consumptions, (b) the classification of historical and socio-economic indicators using the proposed feature selection method, (c) the development of a new combined method using Adaptive Neuro-Fuzzy Inference System and Whale Optimization Algorithm to predict electrical energy consumptions. The simulation results have been tested and validated by real data sets achieved within 1992 and 2010 in two pilot cases in a developing country (Iran) and a developed one (Italy). The research findings pinpointed the greater accuracy and stability of the new developed method for electrical energy consumption forecasting compared to existing single and hybrid benchmark models.
Azim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting. Energy Sources, Part B: Economics, Planning, and Policy 2019, 14, 341 -358.
AMA StyleAzim Heydari, Davide Astiaso Garcia, Farshid Keynia, Fabio Bisegna, Livio De Santoli. Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting. Energy Sources, Part B: Economics, Planning, and Policy. 2019; 14 (10-12):341-358.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. 2019. "Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting." Energy Sources, Part B: Economics, Planning, and Policy 14, no. 10-12: 341-358.
Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.
Azim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy 2019, 251, 113353 .
AMA StyleAzim Heydari, Davide Astiaso Garcia, Farshid Keynia, Fabio Bisegna, Livio De Santoli. A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy. 2019; 251 ():113353.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids." Applied Energy 251, no. : 113353.
Reducing CO2 emissions is a key goal of the strategy for a low-carbon economy and for the choice of greenhouse gas emission mitigation path. An effective forecasting method can represent a useful tool for managing renewable energies in microgrids and mitigating carbon dioxide emission. In this study is evaluated the trend of CO2 emission in Iran, Canada and Italy and compared the CO2 emission from consumption of energy sources: Coal - Natural Gas - Petroleum and other refined hydrocarbons – Renewable Energies. Furthermore, a proposed intelligent method has been provided for CO2 emission forecasting based on Generalized Regression Neural Network and Grey Wolf Optimization. Furthermore, the proposed method has been used for renewable energies generation (Wind power and Solar power) forecasting in the microgrid of Favignana island (Italy). The obtained results confirm the higher accuracy of the proposed method in long-term CO2 emission forecasting and short-term renewable energies generation as compared with other several methods.
Azim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology. Energy Procedia 2019, 159, 154 -159.
AMA StyleAzim Heydari, Davide Astiaso Garcia, Farshid Keynia, Fabio Bisegna, Livio De Santoli. Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology. Energy Procedia. 2019; 159 ():154-159.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. 2019. "Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology." Energy Procedia 159, no. : 154-159.
Maintenance of distribution systems has become more important due to the need to increase energy availability, quality and safety, and also reduce operation cost. Accordingly, the maintenance strategy in distribution systems is one of the most important decision-making activities. One of the most important evaluations for deciding the adequate performance of power distribution system is the identification of the critical components. On the other hand, the budget for maintenance of components is limited, so the maintenance should only be performed on critical components. Identifying the critical components is used to achieve the objective of minimising the cost for maintenance actions. This paper presents a new weighted importance (WI) reliability index model and proposes an appropriate method in order to prioritise the elements of distribution system for reliability-centered maintenance (RCM) at two different levels. At the first level, the feeders of a sample distribution substation are prioritised for the RCM actions. The second level is more detailed than the first level. At the second level, the components of a sample feeder are prioritised for the RCM actions. The reliability of system can be increased to as much as required level while minimal maintenance to be done by the maintenance prioritisation of the system components and performing the maintenance actions on the critical components. Appropriate maintenance decision-making for the critical components of distribution system can lead to the improvement in the reliability of distribution system.
Peyman Afzali; Farshid Keynia; Masoud Rashidinejad. A new model for reliability-centered maintenance prioritisation of distribution feeders. Energy 2019, 171, 701 -709.
AMA StylePeyman Afzali, Farshid Keynia, Masoud Rashidinejad. A new model for reliability-centered maintenance prioritisation of distribution feeders. Energy. 2019; 171 ():701-709.
Chicago/Turabian StylePeyman Afzali; Farshid Keynia; Masoud Rashidinejad. 2019. "A new model for reliability-centered maintenance prioritisation of distribution feeders." Energy 171, no. : 701-709.
A wavelet neural network (WNN) is proposed for short-term price forecasting (STPF) in electricity markets. Back propagation algorithm is used for training the wavelet neural network for prediction. Weights in the back propagation algorithm are usually initialised with small random values. If the random initial weights happen to be far from a suitable solution or near a poor local optimum, training may take a long time or get trapped in the local optimum. In this paper, we show that WNN has acceptable prediction properties compared to other forecasting techniques. We investigated proper weight initialisations of WNN, and proved that it attains a superior prediction performance. Finally, we used a two-step correlation analysis algorithm for input selecting. This algorithm selects the best relevant and non-redundant input features for WNN. Our model is examined for MCP prediction of the Spanish market and LMP forecasting in PJM (Pennsylvania, New Jersey and Maryland) market for the year 2002 and 2006 respectively.
Farshid Keynia; Azim Heydari. A new short-term energy price forecasting method based on wavelet neural network. International Journal of Mathematics in Operational Research 2019, 14, 1 .
AMA StyleFarshid Keynia, Azim Heydari. A new short-term energy price forecasting method based on wavelet neural network. International Journal of Mathematics in Operational Research. 2019; 14 (1):1.
Chicago/Turabian StyleFarshid Keynia; Azim Heydari. 2019. "A new short-term energy price forecasting method based on wavelet neural network." International Journal of Mathematics in Operational Research 14, no. 1: 1.
The forecasting of electricity load is considered as an essential instrument, especially in countries with a restructured electricity market. The mid-term prediction is performed for the period within 1 month to 1 or 2 years and it is important for mid-term planning, including planning of repairs and economic exploitation of power systems, which are related to the reliability of the system directly. The forecast horizon in this paper is monthly and on a daily basis (peak load). The combined method of the neural network and the particle optimization algorithm were used to predict the load, and then the maximum amount of environmental pollution caused by the production of electricity required to supply the predicted load was calculated. The applied method was tested on the data of a North American electric company for four months (four seasons) and in comparison to the other methods presented in previous studies, it had an acceptable accuracy.
Azim Heydari; Farshid Keynia; Davide Astiaso Garcia; Livio De Santoli. Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach. 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA) 2018, 1 -5.
AMA StyleAzim Heydari, Farshid Keynia, Davide Astiaso Garcia, Livio De Santoli. Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach. 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA). 2018; ():1-5.
Chicago/Turabian StyleAzim Heydari; Farshid Keynia; Davide Astiaso Garcia; Livio De Santoli. 2018. "Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach." 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA) , no. : 1-5.
Farshid Keynia. An optimal design to provide combined cooling, heating, and power of residential buildings. International Journal of Modelling and Simulation 2018, 1 -16.
AMA StyleFarshid Keynia. An optimal design to provide combined cooling, heating, and power of residential buildings. International Journal of Modelling and Simulation. 2018; ():1-16.
Chicago/Turabian StyleFarshid Keynia. 2018. "An optimal design to provide combined cooling, heating, and power of residential buildings." International Journal of Modelling and Simulation , no. : 1-16.
This study presents a lifetime efficiency index model for substation equipment and proposes an appropriate method for the optimal maintenance of substation equipment. Along the substation equipment lifetime, due to ageing and daily operations, the failure rate increases gradually. So it is needed to do a maintenance strategy for the aim of reduction in the failure rate and improvement in the reliability of equipment. The maintenance strategy for substation equipment typically consists of preventive and corrective maintenance. By the lifetime efficiency index, one can analyse the lifetime of the equipment under the conditions of with and without maintenance. In this study, the failure rate of the equipment is modelled by the appropriate Weibull distribution. Maintenance can keep equipment in normal condition, but it incurs cost. The maintenance strategy should be optimised for the minimum maintenance cost under the required reliability condition. In order to solve this objective problem, a cuckoo optimisation algorithm (COA) is used and results are evaluated. The results based on an example show that the proposed COA was indeed capable of obtaining higher quality solutions than other methods for the optimal maintenance.
Peyman Afzali; Farshid Keynia. Lifetime efficiency index model for optimal maintenance of power substation equipment based on cuckoo optimisation algorithm. IET Generation, Transmission & Distribution 2017, 11, 2787 -2795.
AMA StylePeyman Afzali, Farshid Keynia. Lifetime efficiency index model for optimal maintenance of power substation equipment based on cuckoo optimisation algorithm. IET Generation, Transmission & Distribution. 2017; 11 (11):2787-2795.
Chicago/Turabian StylePeyman Afzali; Farshid Keynia. 2017. "Lifetime efficiency index model for optimal maintenance of power substation equipment based on cuckoo optimisation algorithm." IET Generation, Transmission & Distribution 11, no. 11: 2787-2795.