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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (10 August 2019) | Viewed by 25158

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Special Issue Editor

Dr. Vassilis S. Kodogiannis

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School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UK
Interests: neural networks; fuzzy systems; genetic algorithms; hybrid systems; machine learning; image/signal processing; bio-signal analysis; chemometrics; control; non-invasive sensing systems; robotics
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Special Issue Information

Dear Colleagues,

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty. Prospective authors are invited to submit original contributions or survey papers for review for publication in this Special Issue. Topics of interest include, but are not limited to, the following:

Power system operation (including unit commitment, economic dispatch, hydro-thermal coordination, maintenance scheduling, congestion management, load/power flow, and state estimation)
Power system planning (including generation expansion planning, transmission expansion planning, reactive power planning, and power system reliability)
Power system control (such as voltage control, load frequency control, stability control, power flow control, and dynamic security assessment)
Power system automation (such as restoration and management, fault diagnosis and reliability, and network security)
Forecasting application (such as short-term load forecasting, electricity price forecasting, long term load forecasting, wind power forecasting, and solar power forecasting)
Distribution system application (such as the operation and planning of a distribution system, demand side management and demand response, network reconfiguration, and the operation and control of a smart grid)
Distributed generation application (such as distributed generation planning, operation with distributed generation, wind turbine plant control, solar photovoltaic power plant control, and renewable energy sources)

Dr. Vassilis S. Kodogiannis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

Neural networks
Deep learning systems
Fuzzy systems
Evolutionary computing
Machine learning
Forecasting
Control
Scheduling
Reliability
Distribution networks

Published Papers (7 papers)

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Research

17 pages, 1828 KiB

Open AccessArticle

Integrated Approach for Network Observability and State Estimation in Active Distribution Grid

by Basanta Raj Pokhrel, Birgitte Bak-Jensen and Jayakrishnan R. Pillai

Energies 2019, 12(12), 2230; https://0-doi-org.brum.beds.ac.uk/10.3390/en12122230 - 12 Jun 2019

Cited by 20 | Viewed by 3582

Abstract

This paper presents a unique integrated approach to meter placement and state estimation to ensure the network observability of active distribution systems. It includes observability checking, minimum measurement utilization, network state estimation, and trade-off evaluation between the number of real measurements used and the accuracy of the estimated state. In network parameter estimation, observability assessment is a preliminary task. It is handled by data analysis and filtering followed by calculation of the triangular factors of the singular, symmetric gain matrix using an algebraic method. Usually, to cover the deficiency of essential real measurements in distribution systems, huge numbers of virtual measurements are used. These pseudo measurements are calculated values, which are based on the network parameters, real measurements, and forecasted load/generation. Due to the application of a huge number of pseudo-measurements, large margins of error exists in the calculation phase. Therefore, there is still a high possibility of having large errors in estimated states, even though the network is classified as being observable. Hence, an integrated approach supported by forecasting is introduced in this work to overcome this critical issue. Finally, estimation of the trade-off in accuracy with respect to the number of real measurements used has been evaluated in order to justify the method’s practical application. The proposed method is applied to a Danish network, and the results are discussed. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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11 pages, 1429 KiB

Open AccessArticle

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

by Arpita Samanta Santra and Jun-Lin Lin

Energies 2019, 12(11), 2040; https://0-doi-org.brum.beds.ac.uk/10.3390/en12112040 - 28 May 2019

Cited by 41 | Viewed by 3737

Abstract

Electricity load forecasting is an important task for enhancing energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists in optimizing the resources and minimizing the energy wastage. The main motivation of this study was to improve the robustness of short-term load forecasting (STLF) by utilizing long short- term memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM’s parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method was trained using actual load and weather data, and the performance results showed that it yielded small mean absolute percentage error on the test data. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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15 pages, 1813 KiB

Open AccessArticle

Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant

by Shahbaz Hussain, Mohammed Al-Hitmi, Salman Khaliq, Asif Hussain and Muhammad Asghar Saqib

Energies 2019, 12(11), 2037; https://0-doi-org.brum.beds.ac.uk/10.3390/en12112037 - 28 May 2019

Cited by 27 | Viewed by 4411

Abstract

This paper presents the optimization of fuel cost, emission of NO_X, CO_X, and SO_X gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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16 pages, 3489 KiB

Open AccessArticle

Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

by Jiaying Deng, Wenhai Zhang and Xiaomei Yang

Energies 2019, 12(10), 2005; https://0-doi-org.brum.beds.ac.uk/10.3390/en12102005 - 25 May 2019

Cited by 22 | Viewed by 2944

Abstract

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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16 pages, 3928 KiB

Open AccessArticle

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

by Zhenhao Tang, Xiaoyan Wu and Shengxian Cao

Energies 2019, 12(9), 1738; https://0-doi-org.brum.beds.ac.uk/10.3390/en12091738 - 08 May 2019

Cited by 7 | Viewed by 2864

Abstract

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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14 pages, 1832 KiB

Open AccessArticle

An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization

by Fengli Jiang, Yichi Zhang, Yu Zhang, Xiaomeng Liu and Chunling Chen

Energies 2019, 12(9), 1690; https://0-doi-org.brum.beds.ac.uk/10.3390/en12091690 - 05 May 2019

Cited by 9 | Viewed by 3294

Abstract

An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative particles and chaos particles were introduced into the population of the developed algorithm, which was to decrease the randomness and promote search efficiency through guiding particle position updating. Moreover, the cluster focus distance-changing rate was responsible for dynamically adjusting inertia weight. Then the convergence rate and accuracy of this algorithm would be elevated by four functions, which would test effectively the proposed. Finally, the optimized algorithm was verified on the RPO of the IEEE 30-bus power system. The performance of PSO, Random weight particle swarm optimization (WPSO) and Linearly decreasing weight of the particle swarm optimization algorithm (LDWPSO) were identified as the referential information, the proposed GSAPSO was more efficient from the comparison. Calculation results demonstrated that higher quality solutions were obtained and convergence rate and accuracy was significantly higher with regard to the GSAPSO algorithm. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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14 pages, 4288 KiB

Open AccessArticle

Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features

by Haiyan Cheng, Yongjie Zhai, Rui Chen, Di Wang, Ze Dong and Yutao Wang

Energies 2019, 12(3), 543; https://0-doi-org.brum.beds.ac.uk/10.3390/en12030543 - 10 Feb 2019

Cited by 24 | Viewed by 3673

Abstract

During an automatic power transmission line inspection, a large number of images are collected by unmanned aerial vehicles (UAVs) to detect existing defects in transmission line components, especially insulators. However, with twin insulator strings in the inspection images, when the umbrella skirts of the rear string are obstructed by the front string, defect detection becomes difficult. To solve this problem, we propose a method to detect self-shattering defects of insulators based on spatial features contained in images. Firstly, the images are segmented according to the particular color features of glass insulators, and the main axes of insulator strings in the images are adjusted to the horizontal direction. Then, the connected regions of insulators in the images are marked. After that, the vertical lengths of the regions, the number of insulator pixels in the regions, as well as the horizontal distances between two adjacent connected regions are selected as spatial features, based on which defect discriminants are formulated. Finally, experiments are performed using the proposed formula to detect self-shattering defects in the insulators, using the spatial distribution of the connected regions to locate the defects. The experiment results indicate that the proposed method has good detection accuracy and localization precision. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)

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