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Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security

CHAOS SOLITONS & FRACTALS 2023年第1期170卷

作者： Raj, Saurav Mahapatra, Sheila Babu, Rohit Verma, Sumit Inst Chem Technol Dept Elect Engn Marathwada Campus Jalna India Alliance Univ Dept Elect & Elect Engn Bangalore India IIT Kanpur Dept Ind & Management Engn Kanpur India

This work consolidates chaotic maps into a contemporary heuristic approach called chimp optimization algorithm (COA). Augmentation of ten chaotic maps to COA forms the hybrid Chaotic COA (CCOA) which generates solutions in search space with enhanced mobility. A bi-level strategy of validating the robustness of the proposed hybrid method is adopted by first benchmarking it on eighteen assorted benchmark test functions and later by performing statistical analysis. The result obtained candidly establishes that merging of chaotic maps (dominantly Chaotic 4 Iterative type-CCOA4) enhances the performance of COA. The inferences congregated from the benchmark functions and statistical analysis facilitates the application of the recommended algorithm to obtain techno-economic solution in reactive power dispatch (RPD) problem which is manifested as a challenging issue in power system operation. The RPD problem is analyzed implementing the proposed approach with series-shunt compensation in a competitive electricity market for secured operation and the proposed Iterative type-CCOA4 reduces the total operating cost by 3.3450 x 103$ with respect to initial operating cost.

关键词： Reactive power dispatch chimp optimization algorithm Chaotic Maps Chaotic chimp optimization algorithm TCSC SVC

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optimization of hybrid energy management system based on high-energy solid-state lithium batteries and reversible fuel cells

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ENERGY 2023年第1期283卷

作者： Li, Xue Li, Minghai Habibi, Mostafa Najaafi, Neda Safarpour, Hamed North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450045 Henan Peoples R China Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710055 Shaanxi Peoples R China UTE Univ Fac Architecture & Urbanism Calle Rumipamba S-N & Bourgeois Quito Ecuador Saveetha Inst Med & Tech Sci Saveetha Dent Coll & Hosp Dept Biomat Chennai 600077 India Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Sharif Univ Technol Ctr Excellence Design Robot & Automat Dept Mech Engn Azadi AvePOB 11365-9567 Tehran Iran Iran Ind Design Co Tehran Iran Imam Khomeini Int Univ Fac Engn Dept Mech Qazvin Iran

Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT), small size, and competitive pricing for the overall source. A hybrid energy storage system (ESS) controlled by an intelligent energy management strategy (EMS) may be substantially included in multi-source EV design and development. Therefore, this paper proposes a hybrid chimp optimization algorithm (ChOA) and Levy walk technique to create an optimum EMS. The proposed technique reduces battery power (BP) stress and increases the LT, which is accomplished by using a hybrid ChOA-Levy walk (ChOA-LW) optimization algorithm with a rule-based approach in accordance with understanding the performance of LIB and SCAP. In order to optimize the rule-based EMS's control settings, the latter strategy is suggested. The control approach can be implemented online once the offline optimization procedure is finished. The presented technique is evaluated via simulation and on an experimental platform by means of a power emulator testbed of a LIB/SCAP hybrid ESS. In terms of BP stress and LT, the findings are compared with a conventional rule-based approach and a mono-source containing a regular high-power LIB. Results obtained demonstrate the effectiveness of the suggested technique, which enables the requested performance to be satisfied with better energy utilization. The assessment results also show notable LT improvements for the LIB, an improvement of up to 19% over the mono-source in reference to a conventional single cell LIB.

关键词： Battery lifetime Energy management strategy Hybrid energy storage system chimp optimization algorithm

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Profit prediction optimization using financial accounting information system by optimized DLSTM

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HELIYON 2023年第9期9卷 e19431页

作者： Tang, Wei Yang, Shuili Khishe, Mohammad Xian Univ Technol Sch Econ & Management Xian 710054 Shaanxi Peoples R China Open Univ Shaanxi Sch Accounting & Finance Xian 710119 Shaanxi Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

Financial accounting information systems (FAISs) are one of the scientific fields where deep learning (DL) and swarm-based algorithms have recently seen increased use. Nevertheless, the application of these hybrid networks has become more challenging as a result of the heightened complexity imposed by extensive datasets. In order to tackle this issue, we present a new methodology that integrates the twin adjustable reinforced chimp optimization algorithm (TARCHOA) with deep long short-term memory (DLSTM) to forecast profits using FAISs. The main contribution of this research is the development of the TAR-CHOA algorithm, which improves the efficacy of profit prediction models. Moreover, due to the unavailability of an appropriate dataset for this particular problem, a newly formed dataset has been constructed by employing fifteen inputs based on the prior Chinese stock market Kaggle dataset. In this study, we have designed and assessed five DLSTM-based optimization algorithms, for forecasting financial accounting profit. The performance of various models has been evaluated and ranked for financial accounting profit prediction. According to our research, the best-performing DL-based model is DLSTM-TARCHOA. One constraint of our methodology is its dependence on historical financial accounting data, operating under the assumption that past patterns and relationships will persist in the future. Furthermore, it is important to note that the efficacy of our models may differ based on the distinct attributes and fluctuations observed in various financial markets. These identified limitations present potential avenues for future research to investigate alternative methodologies and broaden the extent of our findings.

关键词： Deep neural network Financial accounting information Profit prediction chimp optimization algorithm

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Comparison of Meta-Heuristic optimization algorithms for Global Maximum Power Point Tracking of Partially Shaded Solar Photovoltaic Systems

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algorithmS 2023年第8期16卷 376-376页

作者： Nagadurga, Timmidi Devarapalli, Ramesh Knypinski, Lukasz Lakireddy Bali Reddy Coll Engn Dept Elect & Elect Engn Mylavaram 521230 India Inst Chem Technol Dept Elect Elect & Instrumentat Engn Indianoil Odisha Campus Bhubaneswar 751013 India Poznan Univ Tech Inst Elect Engn & Elect PL-60965 Poznan Poland

Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm optimization (CSO), Grey Wolf optimization (GWO) and the proposed chimp optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms.

关键词： maximum power point tracking PV systems cat swarm optimization grey wolf optimization chimp optimization algorithm

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Optimal sizing of an integrated renewable energy system and effective utilization of surplus energy in electric vehicle charging

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2023年 48卷

作者： Saini, Vishal Singal, S. K. Indian Inst Technol Roorkee Hydro & Renewable Energy Dept Roorkee 247667 Uttarakhand India

Renewable energy based power generation has proven to be a viable standalone option in areas where extending the grid is challenging. This study addresses this issue by assessing the possibility of an integrated renewable energy system (IRES) to electrify twelve villages in the Uttarakhand state of India. Four battery energy storage (BES) devices, namely Lead-Acid (LA), Sodium-Sulfur (NAS), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered for storage in this study. Using the chimp optimization algorithm (ChOA) on the MATLAB (c) platform, eight different configurations consisting of solar photovoltaic (SPV) array, a micro-hydropower (MHP) plant, and a biogas generator (BGG) are modeled and optimized. The study reveals that the optimal IRES configuration with the lowest cost and highest performance comprises 676 SPV panels (260 kWp), one MHP plant (25 kW), one BGG (40 kW), and 648 NAS batteries (778 kWh). This configuration has a total system life cycle cost (LCC) of INR 68.77 million and cost of energy (COE) of 16.77 INR/kWh at 0 % loss of power supply probability. The opti-mization problem was run 1-50 times and found that the proposed ChOA algorithm is more robust compared to others, displaying the lowest Best, Worst, and Mean values of LCC (across all eight configurations), convergence rapidity (26th iteration), and least computational time (3481 sec). However, GWO (seven configurations), MFO (34th iteration), and GA (4154 sec) stand very close to ChOA performance in terms of providing minimum LCCs, convergence rapidity, and computational time, respectively. Furthermore, surplus energy (SE) is effectively utilized by incorporating electric vehicles (EVs) as dump load in the system. The proposed integrated charging (IC) strategy outperforms other charging strategies by energizing 134 EVs, utilizing 99.59 % of SE, and reducing the total COE to 10.57 INR/kWh. Finally, the proposed IC strategy results in a net saving of 94,479.39 tons of greenhouse gas emi

关键词： Stand-alone integrated renewable energy system Life cycle cost chimp optimization algorithm Surplus energy Electric vehicles Integrated charging strategy

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An adaptive hybrid fractal model for short-term load forecasting in power systems

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期207卷 107858-107858页

作者： Li, Xiaolan Zhou, Jun Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China

This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation function (CLFIF), iterative learning (IL) and chimp optimization algorithm (ChOA). More precisely, after selecting similar days in power load data, an amendatory CLFIF model is constructed for an-hour-ahead prediction in terms of hourly load curves. Then, iterative learning based on ChOA optimizes the parameters of the amendatory CLFIF model with higher accuracy. Moreover, to confirm effectiveness of the CLFIF-IL-ChOA model, numerical examples are tested on the historical power load data from PJM and ENTSOE. The numerical results show that the proposed method can obtain higher accuracy, compared to some common methods about time series analysis.

关键词： Adaptive Composite linear fractal interpolation function Iterative learning chimp optimization algorithm Short-term load forecasting

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Market-Based Versus Price-Based Optimal Trading Mechanism Design in Microgrid

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 1页

作者： Zhu, Lianyi Changzheng Engn Co Ltd China Acad Launch Vehicle Technol Beijing 101111 Peoples R China

Increasing numbers of microgrids (MGs) and the maturing of technologies would allow MGs to engage in power market bidding as well as ensure their own consumption. A market base microgrid transaction mechanism design according to the blockchain (BC) platform is established in this study and the chimp optimization algorithm is employed for solving the optimal bidding process in the transaction. In this study, a price-based model is presented that organically integrates BC and MG for solving the issue of insufficient utilization of power in MG gaming competitions. Its convergence accuracy and search ability are high. In addition, two of the most significant players on the market will be utilized as research objects in order to build a real multi sources mini-MG network to address the actual time energy costs issue. The example verification is used as a basis for the comparison evaluation.

关键词： Energy storage system market based and price based microgrid management chimp optimization algorithm game competition market

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Solar photo voltaic module parameter extraction using a novel Hybrid chimp-Sine Cosine algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年

作者： Vandrasi, Raja Kumar Kumar, Bali Sravana Devarapalli, Ramesh Gitam Deemed Univ Dept EEE Visakhapatnam 530045 Andhra Pradesh India BIT Sindri Dept Elect Engn Dhanbad Bihar India Lendi Inst Engn & Technol Dept EEE Vizianagaram India

The research on deriving accurate equivalent circuit of solar photovoltaic (PV) modules is increasing due to the necessity of constructing efficient energy conversion devices. The PV panel manufacturers provide data on three essential points on current-voltage (I-V) characteristics for standard temperature conditions (STC). Hence, the research on PV modules for different environmental/operational conditions with equivalent mathematical models are quite complex. Therefore, there is a necessity to derive accurate PV equivalent circuit parameters using novel AI-based approaches. This work proposes a novel hybrid meta-heuristic algorithm, hybrid chimp-Sine cosine algorithm (HCSCA), for PV panel equivalent circuit parameter extraction. A well-known single- and double-diode PV models have been investigated with the proposed algorithm for different categories of PV modules, namely monocrystalline, polycrystalline, and thin film. The parameters derived from the proposed approach result in minimum error over different executions in the order of less than 10(-10), which recommends better implementation in the present scenario. The nature of extracted parameters and I-V characteristics of considered PV panels are examined over different runs, which provided satisfactory performance characteristics with the proposed algorithm and recommended for its practical implementation.

关键词： Photovoltaic cell Parameter extraction Maximum power point chimp optimization algorithm Sine Cosine algorithm

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WPT compensation topology optimized for PV embedded electric vehicle

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期53卷

作者： Jose, Jenson Therattil, Jose P. APJ Abdul Kalam Technol Univ Dept EEE Thiruvananthapuram Kerala India APJ Abdul Kalam Technol Univ Dept ECE Thiruvananthapuram Kerala India

The incorporation of renewable energy sources into Electric Vehicle (EV) integration systems to charge the onboard charger will result in a significant increase in the number of EV and industrial equipment in the next years. Wireless Power Transmission (WPT) technology is a significant source of operation in the field of power transmission, with enormous potential in a wide range of applications. The resonance phenomenon has lately acquired favor as a method of transferring electricity to a load efficiently across a large air gap. This research presents an efficient L2CL-LCL correction in WPT for obtaining electricity from the PV array and charging the EV's battery. To demonstrate its advantages, the L2CL-LCL method is compared to the DSLCL compensation method. The system adjusts the transmit current of the primary coil to maintain stable output power, maximize power transfer efficiency, and power transferred to the load using an Improved Weighted chimp optimization (IWdCH) algorithm that searches for the coupling coefficient 'k' of the elements of the compensation networks. Finally, the proposed system was implemented in Python power electronics software and its superiority was demonstrated by comparing it to conventional optimization algorithms. It outperforms conventional optimization algorithms in terms of efficiency, with anefficiency of 95 %.

关键词： L2CL-LCL Wireless Power Transfer DSLCL Improved Weighted chimp optimization algorithm PV system Coupling Coefficient Mutual inductance chimp optimization algorithm Dragonfly algorithm Sea Lion optimization Without optimization (WO)

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chimp optimization algorithm with Deep Learning-Driven Fine-grained Emotion Recognition in Arabic Corpus

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Wala bin Subait Iman Basheti Hanan Al Sultan Muhammad Alzaidi Jawhara Aljabri Mohammed Assiri Nahla Salih Department of Language Preparation Arabic Language Teaching Institute Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Riyadh Saudi Arabia The University of Sydney NSW Australia Sydney Australia Department of English College of Arts King Faisal University Saudi Arabia Al-Ahsa Saudi Arabia Department of English Language College of Language Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science University College in Umluj University of Tabouk Tabuk Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al Kharj Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Recently, emotion analysis and classification of tweets have become a crucial area of research. The Arabic language had experienced difficulties with emotion classification on Twitter(X), needing preprocessing more than other languages. Emotion detection is a major challenge in Natural Language Processing (NLP), which allows machines to ascertain the emotions expressed in the text. The task includes recognizing and identifying human feelings such as fear, anger, sadness, and joy. The discovered sentiments and feelings expressed in tweets have gained much recognition in recent years. The Arab region has played a substantial role in international politics and the global economy needs to scrutinize the emotions and sentiments in the Arabic language. Lexicon-based and machine-learning techniques are two common models that address the problems of emotion classification. This study introduces a chimp optimization algorithm with a Deep Learning-Driven Arabic Fine-grained Emotion Recognition (COADL-AFER) technique. The presented COADL-AFER technique mainly aims to detect several emotions in Arabic tweets. In addition to its academic significance, the COADL-AFER technique has practical applications in various fields, including enhancing applications of E-learning, aiding psychologists in recognising terrorist performance, improving product quality, and enhancing customer service. The COADL-AFER technique applies the long short-term memory (LSTM) model for emotion detection. Finally, the hyperparameter selection of the LSTM method can be accomplished by COA. The experimental validation of the COADL-AFER system, a crucial step in our research, is verified utilizing the Arabic tweets dataset. The simulation results stated the betterment of the COADL-AFER technique, further reinforcing the reliability of our research.

关键词： Emotion Recognition Deep Learning Natural Language Processing Arabic Language chimp optimization algorithm

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