LFC of a Solar Thermal Integrated Thermal System Considering CSO Optimized TI-DN Controller

Babu, Naladi Ram; Saikia, Lalit Chandra; Bhagat, Sanjeev Kumar; Ramoji, Satish Kumar; Dekaraja, Biswanath; Behra, Manoja Kumar

doi:10.1007/978-981-15-9829-6_25

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…A (1 + PD)-PID cascade controller has been optimized via DSA using ITAE to obtain optimal results. N. R. Babu et al [18] studied the impact of different solar insulation types on the LFC of two-area thermal power systems. Tilt integral minus derivative control (TI-DN) with filter has been applied as a supplementary controller with the crow search optimization (CSO) technique to optimize LFC.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Ali,

Aly,

Little

2024

Energies

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Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.

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Section: Introductionmentioning

confidence: 99%

“…Babu, N.R. et al presented in [18] the effects of different solar insolation types on two thermal area LFC systems. They showed that integrating HVDC with an AC tie-line improved system dynamics.V.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Ali,

Aly,

Little

2024

Energies

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“…Güç sistemlerinde sistem kararlılığını sağlamak amacıyla OÜK konusunda yapılan çalışmalar incelendiğinde, çeşitli evrimsel algoritmaların kullanıldığı görülmektedir. Klasik PI ve PID kontrolörlerin kazançlarının belirlenmesi amacıyla, bakteri arama algoritması (BAA) [3], genetik algoritma (GA) [4], karga arama algoritması (KAA) [5], ateşböceği algoritması (ABA) [4,6], denge algoritması (DA) [7], balina optimizasyonu (BA) [8], parçacık sürü optimizasyonu (PSO) [9,10], gri kurt optimizasyonu (GKO) [11], Jaya algoritması [12,13], yıldırım flaş algoritması (YFA) [14], emperyalist rekabet algoritması (EKA) [15], aşırı nüfus optimizasyonu (ANO) [16] gibi sezgisel yöntemler kullanılmıştır. Klasik kontrolörlerin yanı sıra alan kontrol hatasını (AKH) azaltmak amacıyla kesir dereceli (FO)PID [17,18], TIDN [5], I -TD [19], IPD -(1 + I) [20], bulanık mantık [21,22] gibi kontrolör çeşitleri kullanılmıştır.…”

Section: Giriş (Introduction)unclassified

“…Klasik PI ve PID kontrolörlerin kazançlarının belirlenmesi amacıyla, bakteri arama algoritması (BAA) [3], genetik algoritma (GA) [4], karga arama algoritması (KAA) [5], ateşböceği algoritması (ABA) [4,6], denge algoritması (DA) [7], balina optimizasyonu (BA) [8], parçacık sürü optimizasyonu (PSO) [9,10], gri kurt optimizasyonu (GKO) [11], Jaya algoritması [12,13], yıldırım flaş algoritması (YFA) [14], emperyalist rekabet algoritması (EKA) [15], aşırı nüfus optimizasyonu (ANO) [16] gibi sezgisel yöntemler kullanılmıştır. Klasik kontrolörlerin yanı sıra alan kontrol hatasını (AKH) azaltmak amacıyla kesir dereceli (FO)PID [17,18], TIDN [5], I -TD [19], IPD -(1 + I) [20], bulanık mantık [21,22] gibi kontrolör çeşitleri kullanılmıştır. Abd-Elazim ve Ali tarafından yapılan çalışmada [4] FV ve termal santralin bulunduğu bir güç sisteminde ateşböceği algoritması (ABA) ile PI kontrolör parametrelerinin belirlenmesi amaçlanmıştır.…”

Section: Giriş (Introduction)unclassified

The automatic generation controller based on whale optimization algorithm in PV-thermal power systems

Can

Eroğlu

Öztürk

2022

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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To ensure a balance between the power generated and the consumed power in power systems, a control process namely automatic generation control (AGC) must be carried out. This process becomes more challenging due to increasing use of renewable energy sources (RES) such as wind turbines (WT) and photovoltaic (PV) panels. Therefore, AGC needs to be performed more sensitively. In this study, it has been aimed to determine the parameters of the PID controller by using the whale optimization algorithm (WOA) for the AGC in a hybrid power system consisting of photovoltaic (PV) system and thermal generator. The performance of WOA tuned PID controller is tested under load change in area-1 and area-2. Additionally, comparisons have been made with the performances of other optimization techniques such as firefly algorithm (FA), genetic algorithm (GA) and population extremal algorithm (PEO). The results obtained indicated that the WOA tuned PID controller proposed in the study gave better results than the other methods in terms of overshoot values and settling time of system frequency.

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“…Various evolutionary algorithms, such as genetic algorithm (GA) [3], re y algorithm (FA) [3], population extremal optimization (PEO) [5], particle swarm optimization (PSO) [4,6], hybrid shu ed frog leaping algorithm, teaching-learning based optimization (hybrid SFLA-TLBO) [7], equilibrium optimization algorithm (EOA) [8], grey wolf optimization (GWO) [9], hybrid gravitational-re y (hGFA) [10], differential evolution-arti cial electric eld algorithm (DE-AEFA) [11], Jaya algorithm [12,13], sine-cosine algorithm (SCA) [14,15], lightning ash algorithm (LFA) [16], ant-lion optimization (ALO) [17], and gravitational search algorithm (GSA) [18], have been used to tune the controller parameters. Additionally, different optimization algorithms, such as mine blast algorithm (MBA) [19], salp swarm algorithm (SSA) [20], hybrid moth ame optimization-generalised Hop eld neural network (MFO-GHNN) [21], whale optimization algorithm (WOA) [22], crow search algorithm (CSA) [23,24], marine predator algorithm (MPA) [25],…”

Section: Introductionmentioning

confidence: 99%

Ant-Lion Optimizer-Based Load Frequency Controller for a Hybrid Power System with PV and Thermal Power Plant

Can

Öztürk

Kotb

2022

Preprint

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The process of damping frequency fluctuations in power systems, known as load frequency control (LFC), is an important issue that directly affects the balance between production and consumption for maintaining the stable operation of an interconnected power system. LFC is more complex in multi-area interconnected power systems due to the increasing penetration of renewable energy sources, such as wind turbines and photovoltaic (PV) panels. This study proposes to determine the proportional integral derivative (PID) controller parameters for LFC in a two-area reheat thermal power system, including the photovoltaic system, using ant-lion optimization (ALO). The two-area power system model, consisting of a thermal generator and a PV system, was modelled in MATLAB/SIMULINK. To analyse the effectiveness of the optimization method, the system behavior was examined by considering different case studies, such as the change in load demand and solar irradiation values. The simulation results were compared with the results of different heuristic algorithms, such as the firefly algorithm (FA), genetic algorithm (GA), and population extremal algorithm (PEO). The results revealed that the suggested ALO-PID controller provides about 60% improvement in overshoot/undershoot values and settling time compared to other heuristic optimization methods mentioned.

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LFC of a Solar Thermal Integrated Thermal System Considering CSO Optimized TI-DN Controller

Cited by 4 publications

References 22 publications

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

The automatic generation controller based on whale optimization algorithm in PV-thermal power systems

Ant-Lion Optimizer-Based Load Frequency Controller for a Hybrid Power System with PV and Thermal Power Plant

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