# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Transformers
(b) The confidence factor/truth value of all rule neurons is \(1\). (c) The weight \(1\) is associated with the synapses connecting the rule neurons and proposition neurons, since these weights are not adjusted using the weight adjustment process. (d) The rule neurons \(\sigma^{2}_{r1},\ldots,\sigma^{2}_{rn}\) have spiking rules of the form \(r^{2}_{j}:E^{2}/a^{\theta_{j}}\to a^{\theta_{j}},j=1,2,\ldots,n\), where \(E^{2}=\{\theta_{j}\geq\tau^{2}_{j}\},\tau^{2}_{j}=0,\theta_{j}=(w_{lj}\otimes \alpha_{1})\oplus(w_{2j}\otimes\alpha_{2})\oplus\cdots\oplus(w_{kj}\otimes \alpha_{k})\). The multiplication operator and addition operator of fuzzy truth values are denoted by \(\otimes\) and \(\oplus\) _respectively. Furthermore, the rule neurons \(\sigma_{1}^{4},\ldots,\sigma_{m}^{4}\) have the spiking rules of the form \(r_{1}^{4}:E_{1}^{4}/a^{\beta_{1}}\to a;d_{j}\), where \(E_{1}^{4}=\{\theta_{j}\geq\tau_{1}^{4}\}\) and \(\tau_{1}^{4}=p\)._  
_(3) \(syn=\{(\sigma_{1}^{1},\sigma_{2}^{2}),(\sigma_{1}^{1},\sigma_{2}^{2}),\ldots,(\sigma_{pk}^{1},\sigma_{m}^{2}),(\sigma_{1}^{2},\sigma_{p1}^{3}),(\sigma_{2} ^{2},\sigma_{p1}^{3}),\ldots,(\sigma_{m}^{2},\sigma_{p1}^{3}),(\sigma_{p1}^{ 3},\sigma_{r1}^{4}),\) \((\sigma_{p1}^{3},\sigma_{r2}^{4}),\ldots,(\sigma_{p1}^{3},\sigma_{rn}^{4}),( \sigma_{r1}^{4},\sigma_{p1}^{5}),(\sigma_{r2}^{4},\sigma_{p1}^{5}),\ldots,( \sigma_{m}^{4},\sigma_{p1}^{5})\}\)._  
_(4) \(IN=\{\sigma_{p1}^{1},\ldots,\sigma_{pk}^{1}\},OUT=\{\sigma_{p1}^{5}\}\)._  
In large power transformers, the insulation and heat dissipation are done by transformer oil. Under different thermal and electricity condition, the oil present in the transformer decomposes. If any faults occurs in the oil-immersed power transformers, by analyzing the transformer oil the faults can be identified efficiently. Furthermore, while performing this task the ratios between the gases are given as inputs to the network. In the LSNPs model constructed in [91], the ratio of \(CH_{4}/H_{2},C_{2}H_{4}/C_{2}H_{6},C_{2}H_{2}/C_{2}H_{4},C_{2}H_{2}/\text{Total Hydrocarbon}\), \(C_{2}H_{4}/\text{Total Hydrocarbon}\), \(C_{4}/\text{Total Hydrocarbon}\), \((C_{2}H_{4}+CH_{4})/\text{Total Hydrocarbon}\) are considered as input of the system. In this model, the weights associated with the synapses are trained using the Widrow-Hoff algorithm. The LSNPs model can express the relationship between the DGA data in transformer oil and different types of faults in transformers efficiently. Moreover, LSNPs model with belief AdaBoost is very efficient while diagnosing faults in transformers for thermal and electric fault situations with dissolved gas data (DGA data). Another purpose of using the belief AdaBoost algorithm is to improve the generalization ability. While performing the task of fault diagnosis, the belief AdaBoost is able to avoid the disadvantages of classical AdaBoost algorithm. Moreover, the accuracy of the test set is improved. Altogether, belief AdaBoost improves the generalization ability of learning systems. Finally, if the correctness of diagnosis results are considered, the proposed method in [91] is superior to well-known methods such as improved three-ratio method, back-propagation neural network, support vector machine, deep belief network.