From 589836a0fca932065f8dc0556e71d9fe86d40254 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rodrigo=20Henr=C3=ADquez-Auba?= Date: Mon, 28 Aug 2023 22:47:14 -0400 Subject: [PATCH] update typos (#53) --- docs/src/index.md | 2 +- .../tutorials/tutorial_Incidence_BA_ABA_matrices.md | 6 +++--- docs/src/tutorials/tutorial_LODF_matrix.md | 6 +++--- docs/src/tutorials/tutorial_PTDF_matrix.md | 8 ++++---- docs/src/tutorials/tutorial_VirtualLODF_matrix.md | 10 +++++----- docs/src/tutorials/tutorial_VirtualPTDF_matrix.md | 10 +++++----- src/virtual_ptdf_calculations.jl | 2 +- 7 files changed, 22 insertions(+), 22 deletions(-) diff --git a/docs/src/index.md b/docs/src/index.md index 9c6a3b7f..dfa66f5c 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -8,7 +8,7 @@ CurrentModule = PowerNetworkMatrices `PowerNetworkMatrices.jl` is a [`Julia`](http://www.julialang.org) package for the evaluation of network matrices given the system's data. The package allows to compute -the matrices according to different methods, providing a flexibe and powerful tool. +the matrices according to different methods, providing a flexible and powerful tool. The documentation and code are organized according to the needs of different users depending on their skillset and requirements. In broad terms there are three categories: diff --git a/docs/src/tutorials/tutorial_Incidence_BA_ABA_matrices.md b/docs/src/tutorials/tutorial_Incidence_BA_ABA_matrices.md index c4cd49f1..806cb5cc 100644 --- a/docs/src/tutorials/tutorial_Incidence_BA_ABA_matrices.md +++ b/docs/src/tutorials/tutorial_Incidence_BA_ABA_matrices.md @@ -41,13 +41,13 @@ incidence_matrix.axes # data: Incidence Matrix incidence_matrix.data -# lookup: dictionary linking the branche names and bus numbers with the row +# lookup: dictionary linking the branches names and bus numbers with the row # and column numbers, respectively. incidence_matrix.axes # ref_bus_positions: set containing the positions of the reference buses. # this represents the positions where to add the column of zeros. Please refer to the -# exaple in the BA matrix for more details. +# example in the BA matrix for more details. incidence_matrix.ref_bus_positions ``` @@ -63,7 +63,7 @@ the column related to the reference bus is discarded. ## BA_Matrix The `BA_Matrix` is a structure containing the matrix coming from the product of the -`IncidenceMatrix` and the diagonal matrix contianing the impedence of the system's branches ("B" matrix). +`IncidenceMatrix` and the diagonal matrix containing the impedence of the system's branches ("B" matrix). The `BA_Matrix` is computed as follows: diff --git a/docs/src/tutorials/tutorial_LODF_matrix.md b/docs/src/tutorials/tutorial_LODF_matrix.md index 348edc35..303b6494 100644 --- a/docs/src/tutorials/tutorial_LODF_matrix.md +++ b/docs/src/tutorials/tutorial_LODF_matrix.md @@ -7,7 +7,7 @@ Before diving into this tutorial we encourage the user to load `PowerNetworkMatr As for the `PTDF` matrix, the `LODF` one can be evaluated according to two different approaches: - `Dense`: considers functions for dense matrix multiplication and inversion -- `KLU`: considers functions for sparse matrix multiplication and inversion(**default**) +- `KLU`: considers functions for sparse matrix multiplication and inversion (**default**) The evaluation of the `LODF` matrix can be easily performed starting from importing the system's data and then by simply calling the `LODF` method. @@ -69,7 +69,7 @@ lodf_5 = LODF(a, aba, ba); For those methods that either require the evaluation of the `PTDF` matrix, or that execute this evaluation internally, two different approaches casen be used. -As for the `PTDF` matrix, here too the optional argument `linear_solver` can be specified with either `KLU` (for spars matrix calculation) or `Dense` (for sparse matrix calculation). +As for the `PTDF` matrix, here too the optional argument `linear_solver` can be specified with either `KLU` (for sparse matrix calculation) or `Dense` (for sparse matrix calculation). ``` @repl tutorial_PTDF_matrix lodf_dense = LODF(sys, linear_solver="Dense"); @@ -97,4 +97,4 @@ Please consider that 0.4 was used for the purpose of this tutorial. In practice **NOTE (2):** the `tol` argument does not refer to the "sparsification" tolerance of the `PTDF` matrix that is computed in the `LODF` method. -**NOTE (3):** in case the method `LODF(a::IncidenceMatrix, ptdf::PTDF)` is considerd, an error will be thrown whenever the `tol` argument in the `PTDF` structure used as input is different then 1e-15. \ No newline at end of file +**NOTE (3):** in case the method `LODF(a::IncidenceMatrix, ptdf::PTDF)` is considered, an error will be thrown whenever the `tol` argument in the `PTDF` structure used as input is different than `1e-15`. \ No newline at end of file diff --git a/docs/src/tutorials/tutorial_PTDF_matrix.md b/docs/src/tutorials/tutorial_PTDF_matrix.md index ac286691..47603692 100644 --- a/docs/src/tutorials/tutorial_PTDF_matrix.md +++ b/docs/src/tutorials/tutorial_PTDF_matrix.md @@ -1,7 +1,7 @@ # PTDF matrix In this tutorial the methods for computing the Power Transfer Distribution Factors (`PTDF`) are presented. -Before diving into this tutorial we encourage the user to load `PowerNetworkMatrices`, hit the `?` key in the REPL terminal and look for the documentiont of the different `PTDF` methods avialable. +Before diving into this tutorial we encourage the user to load `PowerNetworkMatrices`, hit the `?` key in the REPL terminal and look for the documention of the different `PTDF` methods available. ## Evaluation of the `PTDF` matrix @@ -59,11 +59,11 @@ get_ptdf_data(ptdf_klu) ``` By default the "KLU" method is selected, which appeared to require significant less time and memory with respect to "Dense". -Please note that either the `KLU` or `Dense` method isi used, the resultig `PTDF` matrix is stored as a dense one. +Please note that either the `KLU` or `Dense` method is used, the resulting `PTDF` matrix is stored as a dense one. ## Evaluating the `PTDF` matrix considering distributed slack bus -Whenever needed, the `PTDF` matrix can be computed with a distributed slack bus. To do so, a vecotr of type `Vector{Float64}` needs to be defined, specifying the weights for each bus of the system. These weights identify how the load on the slakc bus is redistributed accross the system. +Whenever needed, the `PTDF` matrix can be computed with a distributed slack bus. To do so, a vector of type `Vector{Float64}` needs to be defined, specifying the weights for each bus of the system. These weights identify how the load on the slakc bus is redistributed accross the system. ``` @repl tutorial_PTDF_matrix # consider equal distribution accross each bus for this example @@ -78,7 +78,7 @@ Once the vector of the weights is defined, the `PTDF` matrix can be computed by ptdf_distr = PTDF(sys, dist_slack=dist_slack_array); ``` -The diffrence between a the matrix computed with and without the `dist_slack` field defined can be seen as follows: +The difference between a the matrix computed with and without the `dist_slack` field defined can be seen as follows: ``` @repl tutorial_PTDF_matrix # with no distributed slack bus diff --git a/docs/src/tutorials/tutorial_VirtualLODF_matrix.md b/docs/src/tutorials/tutorial_VirtualLODF_matrix.md index 803feddc..20567227 100644 --- a/docs/src/tutorials/tutorial_VirtualLODF_matrix.md +++ b/docs/src/tutorials/tutorial_VirtualLODF_matrix.md @@ -8,7 +8,7 @@ Refer to the different arguments of the `VirtualLODF` methods by looking at the The `VirtualLODF` structure retains many of the similarities of the `VirtualPTDF`. However, its computation is more complex and requires some additional data. -Starting from the system data, the `IncidenceMatrix`, `BA_Matrix` and `ABA_Matrix` (with relative LU factorization matrices) are evaluated. The `ABA_Matrix` and `BA_Matrix` are used for the computation of the diagonal elements of the `PTDF` matrix, and this vector is stored in the `VirtualLODF` structure together with the other structures abovementioned. +Starting from the system data, the `IncidenceMatrix`, `BA_Matrix` and `ABA_Matrix` (with relative LU factorization matrices) are evaluated. The `ABA_Matrix` and `BA_Matrix` are used for the computation of the diagonal elements of the `PTDF` matrix, and this vector is stored in the `VirtualLODF` structure together with the other structures mentioned above. Once the `VirtualLODF` is initialized, each row of the matrix can be evaluated separately and on user request. The algorithmic procedure is the following: 1. Define the `VirtualPTDF` structure @@ -19,12 +19,12 @@ Regarding point 2, if the row has been stored previosly then the desired element The flowchart below shows how the `VirtualLODF` is structured and how it works. Examples will be presented in the following sections. ```@raw html - + ``` ## Initialize `VirtualLODF` and compute/access row/element -As for the `LODF` matrix, at first the `System` data must be loaded. The "RTS GMLC" systems is considered as example: +As for the `LODF` matrix, at first the `System` data must be loaded. The "RTS-GMLC" systems is considered as example: ``` @repl tutorial_VirtualPTDF_matrix using PowerNetworkMatrices @@ -58,9 +58,9 @@ col_number = v_lodf.lookup[2]["A3"] el_C31_2_105_bis = v_lodf[row_number, col_number] ``` -**NOTE**: this example was made for the sake of completeness and considering the actual branch names is reccomended. +**NOTE**: this example was made for the sake of completeness and considering the actual branch names is recommended. -As previosly mentioned, in order to evalute a single element of the `VirtualLODF`, the entire row related to the selected branch must be considered. For this reason it is cached for later calls. +As previosly mentioned, in order to evaluate a single element of the `VirtualLODF`, the entire row related to the selected branch must be considered. For this reason it is cached for later calls. This is evident by looking at the following example: ``` @repl tutorial_VirtualPTDF_matrix diff --git a/docs/src/tutorials/tutorial_VirtualPTDF_matrix.md b/docs/src/tutorials/tutorial_VirtualPTDF_matrix.md index 7afcb5d6..443a6568 100644 --- a/docs/src/tutorials/tutorial_VirtualPTDF_matrix.md +++ b/docs/src/tutorials/tutorial_VirtualPTDF_matrix.md @@ -18,12 +18,12 @@ Regarding point 2, if the row has been stored previosly then the desired element The flowchart below shows how the `VirtualPTDF` is structured and how it works. Examples will be presented in the following sections. ```@raw html - + ``` ## Initialize `VirtualPTDF` and compute/access row/element -As for the `PTDF` matrix, at first the `System` data must be loaded. The "RTS GMLC" systems is considered as example: +As for the `PTDF` matrix, at first the `System` data must be loaded. The "RTS-GMLC" systems is considered as example: ``` @repl tutorial_VirtualPTDF_matrix using PowerNetworkMatrices @@ -57,7 +57,7 @@ el_C31_2_105_bis = v_ptdf[row_number, col_number] **NOTE**: this example was made for the sake of completeness and considering the actual branch name and bus number is reccomended. -As previosly mentioned, in order to evalute a single element of the `VirtualPTDF`, the entire row related to the selected branch must be considered. For this reason it is cached in the `VirtualPTDF` structure for later calls. +As previosly mentioned, in order to evaluate a single element of the `VirtualPTDF`, the entire row related to the selected branch must be considered. For this reason it is cached in the `VirtualPTDF` structure for later calls. This is evident by looking at the following example: ``` @repl tutorial_VirtualPTDF_matrix @@ -75,7 +75,7 @@ v_ptdf_2k = VirtualPTDF(sys_2k); ## `VirtualPTDF` with distributed slack bus As for the `PTDF` matrix, here too each row can be evaluated considering distibuted slack buses. -A vecotr of type `Vector{Float64}` is defined, specifying the weights for each bus of the system. +A vector of type `Vector{Float64}` is defined, specifying the weights for each bus of the system. ``` @repl tutorial_VirtualPTDF_matrix # smaller system for the next examples @@ -94,7 +94,7 @@ v_ptdf_distr = VirtualPTDF(sys_2, dist_slack=dist_slack_array); v_ptdf_orig = VirtualPTDF(sys_2); ``` -Now check the difference with the same row related to the branch `"1"` evaluated withou considering distributed slack bus. +Now check the difference with the same row related to the branch `"1"` evaluated without considering distributed slack bus. ``` @repl tutorial_VirtualPTDF_matrix row_distr = [v_ptdf_distr["1", j] for j in v_ptdf_distr.axes[2]] diff --git a/src/virtual_ptdf_calculations.jl b/src/virtual_ptdf_calculations.jl index 13deeee3..afc37c7e 100644 --- a/src/virtual_ptdf_calculations.jl +++ b/src/virtual_ptdf_calculations.jl @@ -24,7 +24,7 @@ matrix. - `axes<:NTuple{2, Dict}`: Tuple containing two vectors: the first one showing the branches names, the second showing the buses numbers. There is no link between the - order of the vector of the branche names and the way the PTDF rows are + order of the vector of the branches names and the way the PTDF rows are stored in the cache. - `lookup<:NTuple{2, Dict}`: Tuple containing two dictionaries, mapping the branches