Fast Estimation Method for Selection of Optimal Distributed Generation Size Using Kalman Filter and Graph Theory

Ameri, Ahmed Al; Nichita, Cristian; Abbood, Hilal; Atabi, Ali Al

doi:10.1109/uksim.2015.105

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…To calculate the variance of the remaining error, the following error is first given to the 𝑎 = [𝛼 1 , 𝛼 2 , … , 𝛼 𝑛 ] 𝑇 . The amount of variance remaining is calculated as in (13).…”

Section: Mc-ds-cdma Schemementioning

confidence: 99%

“…Two cross coupled and Madgwicks filter for estimation of multi-channel … (Nader Abdullah Kadhim) 263 fading processes they use a least recursive squared solution or the least square algorithm (LMS) and least squares recursive algorithm (RLS) [11], [12]. Kalman filter is an optimised recursive filter used in many areas for estimate optimal state of core variable [13], [14]. Commonly, to explain the development of a signal, Kalman filtering using an autoregressive (AR) modelling is used of faded process time shows that BER is superior to the LMS independent of the model and RLS based on.…”

Section: Introductionmentioning

confidence: 99%

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Two cross coupled and Madgwicks filter for estimation of multi-channel dividing systems

Kadhim

Alrubaie

Al‐Kharsan

2022

IJEECS

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The estimates of Rayleigh fading channels are rapidly changing in multi carrier direct sequence code division multiple access (MC-DS-CDMA) multiplexing systems. The most widely accepted answer to this issue is the conventional solution least square (LS) or mean square error estimator (MMSE) using the recursive least squares algorithm (RLS) or the least mean squares (LMS) algorithm. In much of the previous work, only one Kalman filter was used for estimation. In this paper, a Kalman filter is used with a Madgwicks filter together to satisfy the fading problems. However, this requires a priori evaluation of auto regressive (AR) parameters. A standard solution involves the first matching of the auto-completion function of the applying the AR method to Jakes' problem and then tackling it (YWE). Even more the results procedure is limited to crowd constraints and is related to an AR+ process of noise, an approximation considered. In fact, depending on simulation findings, high-AR models outperform conventional models on the basis of spectral estimate and bit error margins (BER). Nevertheless, in order to save costs of computing, the 5-D model of AR is a possibility. The proposed process outperforms edge of art competitors in terms of bit error rate as demonstrated by results.

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Section: Mc-ds-cdma Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two cross coupled and Madgwicks filter for estimation of multi-channel dividing systems

Kadhim

Alrubaie

Al‐Kharsan

2022

IJEECS

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“…The active power (P) injections in buses can be derived and calculated by multiplying the nodal admittance matrix (B) by the angles (δ) of the buses as follows [23]:…”

Section: Power Flowmentioning

confidence: 99%

Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution

et al. 2017

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Abstract:The growth of electrical demand increases the need of renewable energy sources, such as wind energy, to meet that need. Electrical power losses are an important factor when wind farm location and size are selected. The capitalized cost of constant power losses during the life of a wind farm will continue to high levels. During the operation period, a method to determine if the losses meet the requirements of the design is significantly needed. This article presents a Simulink simulation of wind farm integration into the grid; the aim is to achieve a better understanding of wind variation impact on grid losses. The real power losses are set as a function of the annual variation, considering a Weibull distribution. An analytical method has been used to select the size and placement of a wind farm, taking into account active power loss reduction. It proposes a fast linear model estimation to find the optimal capacity of a wind farm based on DC power flow and graph theory. The results show that the analytical approach is capable of predicting the optimal size and location of wind turbines. Furthermore, it revealed that the annual variation of wind speed could have a strong effect on real power loss calculations. In addition to helping to improve utility efficiency, the proposed method can develop specific designs to speeding up integration of wind farms into grids.

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“…( 9). 𝑃 𝑑𝑜𝑤𝑛𝑠𝑡𝑟𝑒𝑎𝑚 (𝑖, 𝑡) = 𝑃 𝐿𝑜𝑎𝑑 (𝑖, 𝑡) + ∑ 𝑃 𝐿𝑜𝑎𝑑 (𝑗, 𝑡) 𝑘 𝑗=1 (9) where 𝑃 𝑑𝑜𝑤𝑛𝑠𝑡𝑟𝑒𝑎𝑚 (𝑖, 𝑡) is the downstream power connected to bus i at time t and 𝑃 𝐿𝑜𝑎𝑑 (𝑗, 𝑡) is the load power at bus j that is connected to buses at the downstream of bus i.…”

Section: A Sequential Pvdg Placement Algorithmmentioning

confidence: 99%

“…Viral and Khatod used an analytical method to find the optimal size and location of DG, assuming load was constant for the system [8]. Other analytical approaches such as sensitivity analysis and linear or quadratic approximations have been used as well [9], [10]. Populationbased algorithms such as Differential Evolution [11], [12], Particle Swarm Optimization [13], and Shuffled Frog Leaping Algorithm (SFLA) [14] are also popular algorithms for this optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Sequential optimal placement of distributed photovoltaics using downstream power index

Athari

Yang

Wang

2017

2017 North American Power Symposium (NAPS)

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The optimization of the size and location of Photovoltaic (PV) Distributed Generation (DG) is a method for reducing distribution networks loss, cutting costs for utilities, and integrating renewable energy into the power grid. However, this optimization problem is a difficult mixed continuous discrete problem that is difficult to solve efficiently. Herein, we propose a novel Sequential PVDG Placement algorithm which utilizes a Downstream Power Index (DPI) for PV allocation. We compare Sequential PVDG Placement results with those of Shuffled Frog Leaping Algorithm (SFLA), a memetic heuristic algorithm. Our algorithm demonstrates similar accuracy and drastically less computation time compared to SFLA. Given its high accuracy and low computation time, sequential PVDG allocation algorithm may be useful for rapid online power dispatch, long-term planning, and microgrid operations under high penetration of renewable sources.

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Fast Estimation Method for Selection of Optimal Distributed Generation Size Using Kalman Filter and Graph Theory

Cited by 4 publications

References 18 publications

Two cross coupled and Madgwicks filter for estimation of multi-channel dividing systems

Two cross coupled and Madgwicks filter for estimation of multi-channel dividing systems

Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution

Sequential optimal placement of distributed photovoltaics using downstream power index

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